The Role of Geographic Information Systems in Climatology

1 January 2017

Climate change is a problem that is affecting people and the environment in the modern world. The average weather conditions are changing and the intensity of extreme weather events is becoming greater. It remains to be seen if this is a natural progression of the earth over time or if humans are exacerbating it, but it is clear that there is a change occurring in today’s weather patterns.

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The purpose of this literature review is to look at current studies in climatology and the role that Geographic Information Systems (GIS) plays in helping to visualize the effects weather is having on the Earth. Climatology and GIS Climatology and GIS separately are broad fields covering many topics that vary in scope. To receive an overview of climatology and GIS used in conjunction with each other, the article that will be first discussed examines the use of GIS in the field of climatology and meteorology.

The study breaks down climatology and meteorology into the subfields of agriculture, ecology, forestry, health and disease, weather forecasting, hydrology, transport, urban environments, and energy (Chapman 2007). The article looks at applied research in spatial climate data. Results of this study provide evidence that the use of GIS is an integral part of the ongoing study of climatology. GIS has evolved into a powerful management tool used for capturing, modeling, analyzing, and displaying spatial data (Chapman 2007).

Since climatological phenomena happen in spatial variables, it makes GIS a useful application to manage spatial climate datasets. Applications of Climatology and GIS GIS maps have enabled researchers looking at environmental impacts of the variations of climate at a variety of scales, but I will focus on two main aspects of applications within the field of Climatology. The two focuses will be on applications within agriculture and ecology. This section will outline literature that use GIS as a tool to analyze climate datasets.

Agriculture There is potential for GIS to help in the field of agricultural climatology by helping to predict yields, fertility, and future climate models. An example of this is provided by (McKenney 2000) using thin plate splines to model climate gradients in Canada to determine plant hardiness zones. By using a trivariate position of latitude, longitude, and elevation, maps of temperature, and rainfall enabled the mapping of each variable required for plant hardiness at a 1km resolution (McKenney 2000).

The methods that are provided show a repeatable, objective approach to mapping climate and plant hardiness that can be used throughout the world. Spatially reliable estimates of climate provide a better basis for assessing climate impacts, with or without climate change. Elevation is an important part for determining both temperature and precipitation. When overlaying the new map of plant hardiness with the old map of plant hardiness in Canada you can see that changes are more pronounced in western Canada, and are consistent with what is known about climate change.

The zones are associated with probabilities of plant survival in relation to average; broad scale conditions (McKenney 2000). Extreme variations, local topography and human interventions can have a significant impact on plant survival in any particular location. Another example of the use of GIS techniques in agricultural climatology is in McKenney et al. (2007) where they look at traditional plant hardiness zone maps to identify areas that are relatively homogenous with respect to climate conditions that affect plant survival.

Using climate envelopes to map the potential range of plant species in North America in wild and cultivated settings they show the predicated climate change impacts can be incorporated into models. The plant hardiness zones are traditionally defined by using extreme minimum temperature (McKenney 2007). In their different approach to looking at traditional plant hardiness zones, they attempt to develop a generic, transparent, and repeatable process for showing the potential distribution of large numbers of perennial plant species.

In order to accomplish this they summarize the minimum and maximum values for different climate variables at locations where a species is known to survive and then mapping the potential range of the species by identifying all locations with conditions that fall within those extents (McKenney 2007). With the climate envelope approach, it is relatively simple way to incorporate predicted impacts related to climate change.

In the study, (Dockerty 2003), they use climate models to show the potential impacts of climate change on nature reserves by looking at 241 plant species from a sample of 86 nature reserves in Great Britain. Present-day and potential future climatic values over the next 100 years are compared against the climatic ranges of the species to determine future climatic suitability for species at each site. The results showed that warming climate would favor a large portion of plants on Scottish reserves and be less favorable for many plant reserves in the south of England (Dockerty 2003).

They overlayed climate envelopes of the same species found together as fossils to estimate past temperatures at the site from the area of overlap of climatic envelopes. They created the model using ArcGIS to show potential future values of variables at different location derived from three climate change scenarios. Future climate scenarios are constructed and the climatic values from these scenarios are used with the climate space models to determine the likelihood of the species occurrence at the location in the future as the climate changes (Dockerty 2003).

The results in the article show that the models used are capable of providing a tool to help conservation resources towards protecting communities in regions that are least likely to be negatively impacted by climate change. Species respond individually to climate change and can be evaluated at a particular location to show that increases in temperature will lead to climates that are more optimal to some species and less optimal for the same species in different locations. A GIS-based computer model was used to identify climate change adaptation ssues arising in regional agricultural production systems in the article, (Sposito 2009), by integrating land suitability analysis with uncertainty analysis to determine optimal agricultural land at a regional scale.

This study involves the South West region of Victoria, Australia and looks at current and future climate conditions and the adaptation responses to the challenge posed by climate change. The article identifies areas that are under threat of productivity decline and suggest alternative agriculture commodities that would be more suitable under different climate conditions.

The model uses multi-criteria decision making and GIS to map the degree of land suitability for the growth of several agricultural commodities, given current and future climate conditions (Sposito 2009). Agriculture covers 60% of the state’s total land surface and was the reason Victoria, Australia was chosen for this model. The baseline year chosen was 2000 and the future predictions were for the year 2050. The model was created in ArcView GIS and ranked areas in terms of suitability for the growth of the commodity under consideration.

The regional areas that may be unsuitable for a current agricultural system may one day become suitable in the future because of climate change. Exploring the possibility of interpreting climate impacts and presenting it in GIS-based visualizations that can be used at the local level can be found in Dockerty (2005). A GIS database was constructed for Norfolk in Great Britain to show the agricultural landscape that undergoes climate change in the 2020s.

The article focuses on expressing climate change in a visual format because it can be absorbed by the human brain much more efficiently than textual information. They link GIS databases with more sophisticated visualization tools to be able to alter viewpoints, terrain, or positions of buildings and vegetation (Dockerty 2005). It is necessary to create future scenarios that exclude climate change effects alongside scenarios that include climate change effects to be able to prepare for all possible future outcomes.

GIS is also used in agriculture to monitor future air temperature and precipitation that will have potential effects on plant yields. In Ninyerola (2000), the article studies the modeling and mapping of air temperature and total precipitation using GIS techniques. The geographical area that is used is in northeast Spain where they developed multiple regression analysis between meteorological variables and geographical variables to produce a general model that can be used over a relatively large area.

These techniques are used to create accurate and objective monthly and annual climatic maps of air temperature and precipitation that can be used to show where plant species will be able to grow with maximum yields and to show where future problems will occur for plant species due to temperature and precipitation changes over time. Ecology In much the same way that potential crop distribution can be modeled with GIS, ecological biodiversity can be modeled with respect to spatial climate datasets (Chapman 2007).

For example, Eatherall (1996) looks at future climate scenarios that have been applied to five United Kingdom sites to see if they are sensitive to climate change using a model based approach. The model is used to show the effect of the other sub-models on the grassland model. The linked model consists of water balance, grassland, evapotranspiration, and interactions between sub-models (Eatherall 1996). The models show that there is a significant impact on United Kingdom grassland productivity and water resource planning due to impacts of future climate change.

In Cowell (2003), they look at uncertainty theories with GIS for modeling coastal hazards of climate change. They present a GIS model that integrates existing predictive model using a differential approach, random simulation, and fuzzy set theory for predicting coastal hazards as effects of sea-level induced recession and storm erosion (Cowell 2003). The model can be used to predict different scenarios of coastal hazards with uncertainties, but can be used in other fields that involve predictive modeling under uncertainty.

The coastal impact of climate change looks at greater frequency of storm surge, sea-level rise of ocean water, and the changes in wave patterns. The uncertainties that are involved in this study are a result of the highly complex nature of coastal processes that are found across time and space. Grid raster-based modeling techniques were used in this model. The study area, Fingal Bay Beach, is located north of Sydney, Australia and was selected because it represented a typical beach in SE Australia.

This study shows that differential, fuzzy set, and random simulation models can be combined to predict hazard probability due to coastal impact of climate change (Cowell 2003). The demand for developing and using environmentally friendly renewable energy has lead to an increase in the use of wind farms. In the article Baban et. al (2000) looks at applying a GIS-assisted approach to locating wind farms in the United Kingdom. A GIS model was created to apply criteria using two different methods to combine layers that were first weighted as equally important and then were graded as perceived importance.

The criteria used to locate areas that are suitable for wind farms were compiled by sending out questionnaires to both the public and private sector. The major factors that were included were proximity to residential areas, noise, greenbelt, topography, ecology, conservation areas, and distance from electricity gridlines (Baban 2000). GIS was used to give weights to each layer and different scores to each attribute within the layers using reclassification and buffer generation methods. The maps show the most and least suitable areas for locating wind farms.

GIS has the capability to manage large amounts of data while being able to be used for modeling impacts of proposed and operational sites to avoid future climate change problems created by current energy resources. Conclusions In recent years, the use of GIS in a variety of applications involving the process of climatological data has increased. GIS can be used to derive and enhance data by taking climate change data and producing maps that can be used for a variety of different applications.

The use of GIS has increased due to the more commercial presence of GIS products on the market, as well as the advances in computer processing ability. As computers become increasingly able to handle high-resolution datasets, the easier it will be to use GIS in climatology and to manage the manipulation of spatial data. They have shown through applications in agriculture and ecology climatology support the future use of GIS as a legitimate tool to be used in both scholarly and professional applications.

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